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CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ecaade2022_16
id	ecaade2022_16
authors	Bailey, Grayson, Kammler, Olaf, Weiser, Rene, Fuchkina, Ekaterina and Schneider, Sven
year	2022
title	Performing Immersive Virtual Environment User Studies with VREVAL
doi	https://doi.org/10.52842/conf.ecaade.2022.2.437
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 437–446
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learns the relationship between building geometry, typology, and construction type with the Global Warming potential (GWP) in tons of C02 equivalent (tCO2e). The first one, a regression model, can predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly through early predictions of the structure’s material and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Pre-Occupancy Evaluation, Immersive Virtual Environment, Wayfinding, User Centered Design, Architectural Study Design
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia20_74
id	acadia20_74
authors	Bucklin, Oliver; Born, Larissa; Körner, Axel; Suzuki, Seiichi; Vasey, Lauren; T. Gresser, Götz; Knippers, Jan; Menges,
year	2020
title	Embedded Sensing and Control
doi	https://doi.org/10.52842/conf.acadia.2020.1.074
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 74-83.
summary	This paper investigates an interactive and adaptive control system for kinetic architectural applications with a distributed sensing and actuation network to control modular fiber-reinforced composite components. The aim of the project was to control the actuation of a foldable lightweight structure to generate programmatic changes. A server parses input commands and geometric feedback from embedded sensors and online data to drive physical actuation and generate a digital twin for real-time monitoring. Physical components are origami-like folding plates of glass and carbon-fiber-reinforced plastic, developed in parallel research. Accelerometer data is analyzed to determine component geometry. A component controller drives actuators to maintain or move towards desired positions. Touch sensors embedded within the material allow direct control, and an online user interface provides high-level kinematic goals to the system. A hierarchical control system parses various inputs and determines actuation based on safety protocols and prioritization algorithms. Development includes hardware and software to enable modular expansion. This research demonstrates strategies for embedded networks in interactive kinematic structures and opens the door for deeper investigations such as artificial intelligence in control algorithms, material computation, as well as real-time modeling and simulation of structural systems.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	caadria2020_347
id	caadria2020_347
authors	Budig, Michael, Heckmann, Oliver, Ng Qi Boon, Amanda, Hudert, Markus, Lork, Clement and Cheah, Lynette
year	2020
title	Data-driven Embodied Carbon Evaluation of Early Building Design Iterations
doi	https://doi.org/10.52842/conf.caadria.2020.2.303
source	D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 303-312
summary	In the early design phases, Life Cycle Assessment can assist project stakeholders in making informed decisions on choosing structural systems and materials with an awareness of environmental sustainability through their embodied carbon content; yet embodied carbon is difficult to quantify without detailed design information in the early design stages. In response, this paper proposes a novel data-driven tool, prior to the definition of floor plan layouts to perform embodied carbon evaluation of existing building designs based on a Bayesian Neural Network (BNN) regression. The BNN is built from data drawn from existing floor plans of residential buildings, and predicts material volume and embodied carbon from generic design parameters typical in the early design stage. Users will be able to interact with the tool in Grasshopper or as an online resource, input generic design parameters, and obtain comparative visualizations based on the choice of a construction system and its environmental sustainability in a 'shoebox' interface - a simplified three-dimensional representation of a building's primary spatial units generated with the tool.
keywords	Regression; Bayesian Neural Network; High-Rise Residential Buildings
series	CAADRIA
email
last changed	2022/06/07 07:54

_id	ecaade2022_161
id	ecaade2022_161
authors	Kharbanda, Kritika, Papadopoulou, Iliana, Pouliou, Panagiota, Daw, Karim, Belwadi, Anirudh and Loganathan, Hariprasath
year	2022
title	LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs
doi	https://doi.org/10.52842/conf.ecaade.2022.2.601
source	Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 601–610
summary	The new construction that is projected to take place between 2020 and 2040 plays a critical role in embodied carbon emissions. The change in material selection is inversely proportional to the budget, as the project progresses. Given the fact that early-stage design processes often do not include environmental performance metrics, there is an opportunity to investigate a toolset that enables early-stage design processes to integrate this type of analysis into the preferred workflow of concept designers. The value here is that early-stage environmental feedback can inform the crucial decisions that are made in the beginning, giving a greater chance for a building with better environmental performance in terms of its life cycle. This paper presents the development of a tool called LearnCarbon, as a plugin of Rhino3d, used to educate architects and engineers in the early stages about the environmental impact of their design. It facilitates two neural networks trained with the Embodied Carbon Benchmark Study by Carbon Leadership Forum, which learn the relationship between building geometry, typology, and structure with the Global Warming potential in tCO2e. The first one, a regression model, is able to predict the GWP based on the massing model of a building, along with information about typology and location. The second one, a classification model, predicts the construction type given a massing model and target GWP. LearnCarbon can help improve the building life cycle impact significantly, through early predictions of the structure’s material, and can be used as a tool for facilitating sustainable discussions between the architect and the client.
keywords	Machine Learning, Carbon Emissions, LCA, Rhino Plug-in
series	eCAADe
email
last changed	2024/04/22 07:10

_id	acadia20_220p
id	acadia20_220p
authors	Rieger, Uwe; Liu, Yinan
year	2020
title	LightWing II
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 220-225
summary	LightWing II is an immersive XR installation that explores hybrid design strategies equally addressing physical and digital design parameters. The interactive project links a kinetic structure with dynamic digital information in the form of 3D projected imagery and spatial sound. A key component of the project was the development of a new rendering principle that allows the accurate projection of stereoscopic images on a moving target screen. Using simple red/cyan cardboard glasses, the system expands the applications of contemporary AR headsets beyond an isolated viewing towards a communal multi-viewer event. LightWing`s construction consists of thin flexible carbon fibre rods used to tension an almost invisible mesh screen. The structure is asymmetrically balanced on a single pin joint and monitored by an IMU. A light touch sets the delicate wing-like object into a rotational oscillation. As a ‘hands-on’ experience, LightWing II creates a mysterious sensation of tactile data and enables the user to navigate through holographic narratives assembled in four scenes, including the interaction with swarms of three winged creatures, being immersed in a silky bubble, and a journey through a velvet wormhole. The user interface is dissolved through the direct linkage between the physical construction and the dynamic digital content. The project was developed at the arc/sec Lab at the University of Auckland. The Lab explores user responsive constructions where dynamic properties of the virtual world influence the material world and vice versa. The Lab’s vision is to re-connect the intangible computer world to the multisensory qualities of architecture and urban spaces. With a focus on intuitive forms of user interaction, the arc/sec Lab uses large-scale prototypes and installations as the driving method for both the development and the demonstration of new cyber-physical design principles.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	ecaade2020_445
id	ecaade2020_445
authors	Spiegelhalter, Thomas, Andia, Alfredo, Levente, Juhasz and Namuduri, Srikanth
year	2020
title	Part 1: The Integrated Decision Support System - Generative and synthetic biological design imaginations for the Miami bay area
doi	https://doi.org/10.52842/conf.ecaade.2020.2.011
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 11-20
summary	In less than 150 years our carbon society transformed the planet. Today more than 50% of ecologies in the world are determined by unsustainable industrialization processes. The latest IPCC reports show that we are quickly arriving at points of no return in the warming of our planet. We cannot afford to continue in the same direction, we need a new imagination. As part of an E.U.-US funded $1.9 million research project we have been working on multiple projects for the future of the Miami islands since 2018:1. We developed a generative GIS-BIM based Python API for mapping and optimization of carbon-neutral design workflows. It includes genetic design combinatorics with intuitive graphical Dynamo-Python-Grasshopper programming with experimental design results. 2. We worked on a series of design research for the Miami Bay that envisions islands, living shorelines, programmable soils, and infrastructures that grow by themselves using synthetic biology.
keywords	Automated Workflows, Synthetic Biology, Artificial Intelligence, Architecture, Sea-level Rise
series	eCAADe
email
last changed	2022/06/07 07:56

_id	acadia20_148p
id	acadia20_148p
authors	Vansice, Kyle; Attraya, Rahul; Culligan, Ryan; Johnson, Benton; Sondergaard, Asbjorn; Peters, Nate
year	2020
title	Stereoform Slab
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 148-153
summary	Stereoform Slab is both a pavilion and a prototype - an exhibition for the 2019 Chicago Architectural Biennial. It is an experiment in how digital form-finding and robotics can be leveraged to rethink the future of concrete construction. Stereoform Slab examines the role of one of the most ubiquitous horizontal elements in the city - the concrete slab, also the most common element in contemporary construction. Using smarter forming systems - in this case, a ruled-surface-derived, robotic hotwire process - the Stereoform Slab prototype proved that the amount of material used and waste generated could be minimized without increasing construction complexity, by about 20% over a conventional system. Stereoform also extends the conventional concrete span (column spacing), specifically in Chicago, from 30’ to 45’. In developing a concrete forming system that affords added flexibility without increasing construction costs, it is possible to reduce embodied carbon significantly. The method allows reducing carbon in buildings that aren’t typically the subject of advanced architectural design or rigorous optimization – conventional buildings that compose a majority of our built environment, and its respective contributions to global carbon emissions. Stereoform is the result of a multi-objective design optimization process. Optimal materialization, according to the compressive/tensile physics present in beam design, was balanced against the fabrication constraints of a singularly ruled-surface, which enables fast form-making using robotic hotwire cutting. SOM and Autodesk collaborated to mirror the approach developed to optimize Stereoform slab as a pavilion, to the building scale, using the multi-objective optimization platform Refinery. Project Refinery allowed the team to create a hyper-responsive system design that could adapt to any number of varying programmatic conditions and loading patterns. The development of this approach is a crucial step in making optimization techniques flexible enough to balance the number of competing parameters in the design process available and accessible to a broader design audience within architecture and engineering.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	acadia20_198p
id	acadia20_198p
authors	Birkeland, Jennifer; Scelsa, Jonathan A.
year	2020
title	Live L’oeil – Through the Looking Ceiling
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 198-201
summary	Following the proliferation of linear perspective during the Renaissance, the hegemony of the vantage point was often problematically used to signify the patron’s dominance. During the mannerist era, we witnessed the creation of elaborate rooms, painted in architectural linear perspective establishing the illusionary space of faraway lands - a measure of optic imperialism wherein the conquests of the west played out in the domestic decoration of the elite later provided to the public as a societal spectacle in the form of the panorama. Within these architectural illusions, or Quadratura as they were named in Italy, lies the most notable and justifiable critique of design by vantage point, the question ‘which vantage point is privileged?’ History not surprisingly reveals that the typical vantage point was most problematically centered at one and three-quarter meters above the ground – coincident with five centimeters below the average height of a human European male. The design of architectural form through view or spatial image has arguably perpetuated this act of optic bias. This project addresses this problematic practice of design by vantage point by utilizing motion sensors to liberate the virtual space of a canonic example of quadrature from its confines within a singular vantage point. The authors digitally modeled the projective space of Andrea Pozzo’s vision for the Church of Sant’Ignazio di Loyola in Rome, scaled and fit to a gallery space outfitted with a canvas to inform a ceiling plane. Anamorphic images of the virtual heavenly space, as seen through the canvas ceiling picture plane, were created from the digital model and encoded to the individual moments in the room. Individuals who moved through the gallery were followed by the illusion of the heavenly space, creating a live l’oeil distortion.
series	ACADIA
type	project
email
last changed	2021/10/26 08:08

_id	cdrf2019_17
id	cdrf2019_17
authors	Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng
year	2020
title	Pipes of AI – Machine Learning Assisted 3D Modeling Design
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_2
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2020_131
id	ecaade2020_131
authors	Gortazar-Balerdi, Ander and Markusiewicz, Jacek
year	2020
title	Legible Bilbao - Computational method for urban legibility
doi	https://doi.org/10.52842/conf.ecaade.2020.1.209
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 209-218
summary	Legibility is a core concept in spatial cognition theories since Kevin Lynch published The Image of the City in 1960. It is the ability of a city to be interpreted and easily used, travelled and enjoyed, from the pedestrian's perspective. Following a proposal in the participatory budget process of the city of Bilbao, we wrote a technical report to improve the urban legibility of the city and facilitate wayfinding through innovations in signage. This paper aims to present this project, which is an application of computational methods to measure urban legibility that resulted in a proposal for a new wayfinding strategy for Bilbao. The method is based on GIS data, and it simulates urban processes using dedicated algorithms, allowing us to perform two analyses that resulted in two overlapping maps: a heat map of decision points and a map of visual openings. It allowed us to perceive common urban elements that can help to decide both the location of the wayfinding signage and how it should provide the relevant information. In addition, the research introduces the concept of anticipation points, as a complement to the existing idea of decision points.
keywords	Wayfinding; Urban legibility; Spatial cognition
series	eCAADe
email
last changed	2022/06/07 07:51

_id	acadia20_446
id	acadia20_446
authors	Norell, Daniel; Rodhe, Einar; Hedlund, Karin
year	2020
title	Completions
doi	https://doi.org/10.52842/conf.acadia.2020.1.446
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 446-455.
summary	Reuse of construction and demolition waste tends to be exceptional rather than systemic, despite the fact that such waste exists in excess. One of the challenges in handling used elements and materials is integrating them into a digital workflow through means of survey and representation. Techniques such as 3D scanning and robotic fabrication have been used to target irregular geometries of such extant material. Scanning can be applied to digitally define a unique rather than standard stock of materials or, as in the field of preservation, to transfer specific forms and qualities onto a new stock. This paper melds these two approaches through Completions, a project that promotes reuse by integrating salvaged elements and materials into new assemblies. Drawing from the ancient practice of reuse known as spolia, the work develops from the identification and documentation of a varied set of used entities that become points of departure for subsequent design and production of new entities. This involves multiple steps, from locating and selecting used elements to scanning and fabrication. Three assemblies based on salvaged objects are produced: a window frame, a door panel, and a mantelpiece. Different means of documentation are outlined in relation to specific qualities of these objects, from photogrammetry to image and mesh-based tracing. Authentic qualities belonging to these elements, such as wear and patina, are coupled with more ambiguous forms and materialities only attainable through digital survey and fabrication. Finally, Completions speculates on how more automated workflows might make it feasible to develop extensive virtual catalogs of used objects that designers could interact with remotely.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_018
id	ecaade2020_018
authors	Sato, Gen, Ishizawa, Tsukasa, Iseda, Hajime and Kitahara, Hideo
year	2020
title	Automatic Generation of the Schematic Mechanical System Drawing by Generative Adversarial Network
doi	https://doi.org/10.52842/conf.ecaade.2020.1.403
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 1, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 403-410
summary	In the front-loaded project workflow, mechanical, electrical, and plumbing (MEP) design requires precision from the beginning of the design phase. Leveraging insights from as-built drawings during the early design stage can be beneficial to design enhancement. This study proposes a GAN (Generative Adversarial Networks)-based system which populates the fire extinguishing (FE) system onto the architectural drawing image as its input. An algorithm called Pix2Pix with the improved loss function enabled such generation. The algorithm was trained by the dataset, which includes pairs of as-built building plans with and without FE equipment. A novel index termed Piping Coverage Rate was jointly proposed to evaluate the obtained results. The system produces the output within 45 seconds, which is drastically faster than the conventional manual workflow. The system realizes the prompt engineering study learned from past as-built information, which contributes to further the data-driven decision making.
keywords	Generative Adversarial Network; MEP; as-built drawing; automated design; data-driven design
series	eCAADe
email
last changed	2022/06/07 07:57

_id	acadia20_160p
id	acadia20_160p
authors	Scelsa, Jonathan A.; Birkeland, Jennifer
year	2020
title	The Collective Perspective Machine
source	ACADIA 2020: Distributed Proximities / Volume II: Projects [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95253-6]. Online and Global. 24-30 October 2020. edited by M. Yablonina, A. Marcus, S. Doyle, M. del Campo, V. Ago, B. Slocum. 160-163
summary	Since the age of humanism, both on the easel and our screens, the production of the architectural image has been conventionally governed by one individual, whom we might refer to as the drafter. As the primary author sitting in the chair of the vantage point, the drafter occupies the privileged position, for whom the translation between the second and third dimensions establishes an approximate realism. The viewers, or secondary participants, by contrast, are relegated to a subordinate position, subject to the residual distortions of the drafter’s vision, based on their relative vantage points. While perhaps cynical, our current condition does not share the same philosophical positivistic optimism of the Renaissance, nor the ideal faith in humanity that empowered the democratic universalisms of modernity. Rather, it is formed from an ambiguous inquiry into creating a new sense of truth, brought forth by the proliferation and amplification of multiple individual ‘perspectives.’ In his conclusion to The Projective Cast, Evans illustrates ten ‘transitive spaces’ of geometric projection towards the generation and representation of a designed object. The fifth “transitive space” describes the space between a building or object and its defined perspectival representations. Evans observes that this path typically follows the progression from the object to a photo or a drawing and is rarely reversed. This project and machine designed for an exhibition seeks to establish a new procedure for generating design, neither subjectively from a personal static individual point nor objectively in the round for all to experience equally. Instead, a new machine establishes form as the hybrid of multiple responsive perspectives wherein all viewers are simultaneously the generator of projective form and the receiver of distorted images.
series	ACADIA
type	project
email
last changed	2021/10/26 08:03

_id	sigradi2020_254
id	sigradi2020_254
authors	Costa, Eduardo; Shepherd, Paul; Velasco, Rodrigo; Hudson, Roland
year	2020
title	Automating Concrete Construction: Sustainable social housing in Colombia
source	SIGraDi 2020 [Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics - ISSN: 2318-6968] Online Conference 18 - 20 November 2020, pp. 254-259
summary	The construction industry is a major source of carbon, and the main culprit is concrete. In addition, productivity for the construction sector is poor, and concrete construction in particular is labour intensive, slow, and costly. This paper introduces ongoing research addressing these two fundamental issues. First, by developing an integrated framework for automating manufacturing of reinforced concrete building elements through computation and robotic technology, and second by adapting such framework to the specific technical and socio- economic contexts of Colombian construction, specifically for social housing.
keywords	Non-prismatic concrete elements, Reinforced concrete, Flexible formwork, Parametric modelling, Construction in Colombia
series	SIGraDi
email
last changed	2021/07/16 11:48

_id	cdrf2019_297
id	cdrf2019_297
authors	H. Mohamed, D. W. Bao, and R. Snooks
year	2020
title	Super Composite: Carbon Fibre Infused 3D Printed Tectonics
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_28
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	This research posits an innovative process of embedding carbon fibre as the primary structure within large-scale polymer 3D printed intricate architectural forms. The design and technical implications of this research are explored and demonstrated through two proto-architectural projects, Cloud Affects and Unclear Cloud, developed by the RMIT Architecture Snooks Research Lab. These projects are designed through a tectonic approach that we describe as a super composite – an approach that creates a compression of tectonics through algorithmic selforganisation and advanced manufacturing. Framed within a critical view of the lineage of polymer 3D printing and high tech fibres in the field of architectural design, the research outlines the limitations of existing robotic processes employed in contemporary carbon fibre fabrication. In response, the paper proposes an approach we describe asInfused Fibre Reinforced Plastic (IFRP) as a novel fabrication method for intricate geometries. This method involves 3D printing of sacrificial formwork conduits within the skin of complex architectural forms that are infused with continuous carbon fibre structural elements. Through detailed observation and critical review of Cloud Affects and Unclear Cloud (Fig. 2), the paper assesses innovations and challenges of this research in areas including printing, detailing, structural analysis and FEA modelling. The paper notes how these techniques have been refined through the iterative design of the two projects, including the development of fibre distribution mapping to optimise the structural performance.
series	cdrf
email
last changed	2022/09/29 07:51

_id	ecaade2023_227
id	ecaade2023_227
authors	Moorhouse, Jon and Freeman, Tim
year	2023
title	Towards a Genome for Zero Carbon Retrofit of UK Housing
doi	https://doi.org/10.52842/conf.ecaade.2023.2.197
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 197–206
summary	The United Kingdom has some of the worst insulated housing stock in Northern Europe. This is in part due to the age of housing in the UK, with over 90% being built before 1990 [McCrone 2017, Piddington 2020]. Moreover, 85% of current UK housing will still be in use in 2050 by which stage their Government are targeting Net Carbon Zero [Eyre 2019]. Domestic energy use accounts for around 25% of UK carbon emissions. The UK will need to retrofit 20 million dwellings in order to meet this target. If this delivery were evenly spread, it would equate to over 2,000 retrofit completions each day. Government-funded initiatives are stimulating the market, with upwards of 60,000 social housing retrofits planned for 2023, but it is clear that a system must be developed to enable the design and implementation of housing-stock improvement at a large scale.This paper charts the 20-year development of a digital approach to the design for low-carbon domestic retrofit by architects Constructive Thinking Studio Limited and thence documents the emergence of a collaborative approach to retrofit patterns on a National scale. The author has led the Research and Development stream of this practice, developing a Building Information Modelling methodology and integrated Energy Modelling techniques to optimise design for housing retrofit [Georgiadou 2019, Ben 2020], and then inform a growing palette of details and a database of validated solutions [Moorhouse 2013] that can grow and be used to predict options for future projects [D’Angelo 2022]. The data is augmented by monitoring energy and environmental performance, enabling a growing body of knowledge that can be aligned with existing big data to simulate the benefits of nationwide stock improvement. The paper outlines incremental case studies and collaborative methods pivotal in developing this work The proposed outcome of the work is a Retrofit Genome that is available at a national level.
keywords	Retrofit, Housing, Zero-Carbon, BIM, Big Data, Design Genome
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2020_017
id	ecaade2020_017
authors	Chan, Yick Hin Edwin and Spaeth, A. Benjamin
year	2020
title	Architectural Visualisation with Conditional Generative Adversarial Networks (cGAN). - What machines read in architectural sketches.
doi	https://doi.org/10.52842/conf.ecaade.2020.2.299
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 299-308
summary	As a form of visual reasoning, sketching is a human cognitive activity instrumental to architectural design. In the process of sketching, abstract sketches invoke new mental imageries and subsequently lead to new sketches. This iterative transformation is repeated until the final design emerges. Artificial Intelligence and Deep Neural Networks have been developed to imitate human cognitive processes. Amongst these networks, the Conditional Generative Adversarial Network (cGAN) has been developed for image-to-image translation and is able to generate realistic images from abstract sketches. To mimic the cyclic process of abstracting and imaging in architectural concept design, a Cyclic-cGAN that consists of two cGANs is proposed in this paper. The first cGAN transforms sketches to images, while the second from images to sketches. The training of the Cyclic-cGAN is presented and its performance illustrated by using two sketches from well-known architects, and two from architecture students. The results show that the proposed Cyclic-cGAN can emulate architects' mode of visual reasoning through sketching. This novel approach of utilising deep neural networks may open the door for further development of Artificial Intelligence in assisting architects in conceptual design.
keywords	visual cognition; design computation; machine learning; artificial intelligence
series	eCAADe
email
last changed	2022/06/07 07:55

_id	cdrf2019_36
id	cdrf2019_36
authors	Dan Luo, Joseph M. Gattas, and Poah Shiun Shawn Tan
year	2020
title	Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing
doi	https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_4
source	Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020)
summary	Non-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of fulllength structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and rejoining processes. The proposed workflow allows the knowledge of worker to be translated into a classifier that automatically recognizes and removes areas of defects based on image capture. In addition, a real-time optimization algorithm in decision making is developed to assign a joining sequence of fragmented timber from a dynamic inventory, creating a single piece of targeted length with a significant reduction in material waste. In addition to an industrial application, this workflow also allows for future inventory-constrained customizable fabrication, for example in production of non-standard architectural components or adaptive reuse or defect-avoidance in out-of-grade timber construction.
series	cdrf
email
last changed	2022/09/29 07:51

_id	acadia20_594
id	acadia20_594
authors	Farahbakhsh, Mehdi; Kalantar, Negar; Rybkowski, Zofia
year	2020
title	Impact of Robotic 3D Printing Process Parameters on Bond Strength
doi	https://doi.org/10.52842/conf.acadia.2020.1.594
source	ACADIA 2020: Distributed Proximities / Volume I: Technical Papers [Proceedings of the 40th Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 978-0-578-95213-0]. Online and Global. 24-30 October 2020. edited by B. Slocum, V. Ago, S. Doyle, A. Marcus, M. Yablonina, and M. del Campo. 594-603.
summary	Additive manufacturing (AM), also known as 3D printing, offers advantages over traditional construction technologies, increasing material efficiency, fabrication precision, and speed. However, many AM projects in academia and industrial institutions do not comply with building codes. Consequently, they are not considered safe structures for public utilization and have languished as exhibition prototypes. While three discrete scales—micro, mezzo, and macro—are investigated for AM with paste in this paper, structural integrity has been tackled on the mezzo scale to investigate the impact of process parameters on the bond strength between layers in an AM process. Real-world material deposition in a robotic-assisted AM process is subject to environmental factors such as temperature, humidity, the load of upper layers, the pressure of the nozzle on printed layers, etc. Those factors add a secondary geometric characteristic to the printed objects that was missing in the initial digital model. This paper introduces a heuristic workflow for investigating the impacts of three selective process parameters on the bond strength between layers of paste in the robotic-assisted AM of large-scale structures. The workflow includes a method for adding the secondary geometrical characteristic to the initial 3D model by employing X-ray computerized tomography (CT) scanning, digital image processing, and 3D reconstruction. Ultimately, the proposed workflow offers a pattern library that can be used by an architect or artificial intelligence (AI) algorithms in automated AM processes to create robust architectural forms.
series	ACADIA
type	paper
email
last changed	2023/10/22 12:06

_id	ecaade2020_432
id	ecaade2020_432
authors	Fragkia, Vasiliki and Worre Foged, Isak
year	2020
title	Methods for the Prediction and Specification of Functionally Graded Multi-Grain Responsive Timber Composites
doi	https://doi.org/10.52842/conf.ecaade.2020.2.585
source	Werner, L and Koering, D (eds.), Anthropologic: Architecture and Fabrication in the cognitive age - Proceedings of the 38th eCAADe Conference - Volume 2, TU Berlin, Berlin, Germany, 16-18 September 2020, pp. 585-594
summary	The paper presents design-integrated methods for high-resolution specification and prediction of functionally graded wood-based thermal responsive composites, using machine learning. The objective is the development of new circular design workflow, employing robotic fabrication, in order to predict fabrication files linked to material performance and design requirements, focused on application for intrinsic responsive and adaptive architectural surfaces. Through an experimental case study, the paper explores how machine learning can form a predictive design framework where low-resolution data can solve material systems at high resolution. The experimental computational and prototyping studies show that the presented image-based machine learning method can be adopted and adapted across various stages and scales of architectural design and fabrication. This in turn allows for a design-per-requirement approach that optimizes material distribution and promotes material economy.
keywords	material specification; responsive timber composites; machine learning; robotic fabrication; building envelopes
series	eCAADe
email
last changed	2022/06/07 07:50

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